فهرست مطالب اسماعیل قجر

Forest roads are essential infrastructures for accessing and managing forest units. Sustainable road management requires accurate spatial information from the road network. Various methods, differing in accuracy, precision, efficiency, and cost, are available for providing the location of roads. Given the remote location of forest roads, the lack of reference stations, potential inaccessibility to online networks, and conditions affecting positioning error, it is necessary to investigate the efficiency of existing positioning methods.

In this research, we studied the accuracy of PPK, RTK, online platforms such as Shamim, GPS, and mobile apps in positioning 34 points on 5 km of forest roads in watershed No. 30 in the Hyrcanian forest. The coordinates were first extended to the study area using the rapid static method. After post-processing and determining reference points, the exact coordinates of all ground control points on the road were obtained through the direct method. Then, the coordinates of all points were measured three times using various methods.

The health survey's statistical results showed that the highest mean differences were 2.53 m and 2.38 m for GPS and mobile apps, respectively. The least mean difference was 0.048 m, 0.049 m, and 0.092 m for PPK, RTK, and Shamim, respectively. The RMSE amount showed that the results from GPS and mobile apps had the least accuracy, and the methods PPK, RTK, and Shamim had the highest. Based on the one-sample T-test and Wilcoxon’s analysis, the amount of differences was not the same within each method through the 34 investigated points. Accuracy analysis indicated that GPS, with a distance mean of 1.14 m, had the least precision, and Shamim and RTK, with 0.0621 m and 0.063 m, were the most precise methods. The analysis of variance showed that the mobile app, Shamim, and PPK had the highest convergence.

The results of this research indicate that the PPK method was the most accurate, and the Shamim method was the most precise. Therefore, these methods can be used for purposes such as optimizing the forest road plan and longitudinal profile that require accurate location information.

This study aimed to evaluate of unsurfaced forest road distresses on the existing curves. Numbers of 30 selective plots on the center of curves were measured. A paired sample plot beside of each curve was measured. In each plot, seven indices of unsurfaced road distress including improper cross section, inadequate roadside drainage, corrugation, dust, potholes, ruts and loose aggregate of soil were measured. Paired samples t-test and Wilcoxon test were used to analysis of data. Result showed that there were improper cross section and dust distresses factors in all plots on the curves, while 3.3 % of paired plots had none of these distresses. Also, distress intensity causing by improper cross section and inadequate roadside drainage factors were more in the curves than to paired plots and significant difference between the curves and paired plots was observed. The results of corrugation indicated that 70 and 66.7 % of sampled plots in the curves points and side plots lacked this type of distress, respectively. Results showed that the depth and area of potholes and ruts, and loose of soil aggregate in the curves points were greater than the paired plots but there was no significant difference between the curves and paired plots. Results of this study shows importance of regular maintenance activities and application of evaluation indices at the network level of pavement.

Hyrcanian (Caspian) area is one of the most important vegetation areas in Iran, which due to its antiquity, has a high ecosystem value. On the other hand, this ecosystem is involved in multiple fires every year and loses a significant level of vegetation, so the use of scientific methods to predict places with potential fire risk is very important. This can be used for the conservation management of Hyrcanian forests. Many real-world systems are used in terms of pattern recognition, so proper use of machine learning methods is essential in practical applications. However, the use of clustering-based methods is emphasized as an effective method due to its approach in pattern recognition and output discovery. The purpose of this study was to evaluate the ability and compare the performance of Fuzzy C-Means and k-Medoids clustering in modeling forest fire occurrence with emphasis on the performance capabilities of the algorithm. Due to the existence of periodic fires, the mentioned algorithms were used to improve the level of coding in MATLAB software in order to improve studies in the field of forest fire risk prediction. Model input criteria in this study are recorded fire points, distance to agricultural areas, distance to the road, distance to the river, air pressure, solar radiation, slope, aspect, wind speed, forest type and percentage of canopy density. The results obtained from the fire hazard prediction map of both algorithms show their high ability to predict the fire occurrence model. Also, based on the results of the confusion matrix table of the comparison of the two algorithms, the FCM algorithm showed better performance than the k-medoids algorithm in predicting places with potential fire risk. Therefore, the use of FCM algorithm is suggested as one of the effective methods in differential clustering for future studies.

The present study compares the performance of three statistical models including logistic regression, frequency ratio and maximum entropy in preparing the zoning map of landslide sensitivity on the watersheds of 24 and 25 (Malak-roud and Shen-roud) regions of Guilan province. For this purpose, all the roads of the two areas were survyed and the number of 844 landslides were detected. Then effective data layers on landslide formation were prepared and then zoning maps of the landslide sensitivity were extracted to the three methods. The results showed that the logistic regression model has an accuracy of 74.6 percent of the Area under ROC Curve in prediction of sensitive areas compared to the accuracy of 68.2 and 65.2 percent by the probabilistic frequency ratio model and Entropy model, respectively. Moreover, based on the logistic regression model, the distance and slope factors have the highest effect on the landslide of forest road trenches. According to this study, a total of 4.32 square kilometers (43.25%) of the landslide areas is categorized in very high and high susceptible groups which have occurred along 51.38 percent of the area. Despite the lack of consideration to superstructure conditions in the present study, a notable percentage of existing roads are exposed to the collapse of trenches and require adequate funding for repair operations. According to the logging ban as a new approach in forest utilization and lack of revenue regeneration of alternative conservation projects great dangers threaten the forest engineering installations and road structures.

The management of Hyrcanian forests is experiencing the first period of logging ban since 2017. The transition from traditional forestry to modern forestry and the suitable management strategies is an indispensable step to prevent the forestry plans from mismanagement. Aiming at strategic planning, this study deals with factors affecting forestry with the aid of value engineering and including experts' viewpoints and study records screened through fuzzy Delphi. The result of the concurrency of the scores of the internal and external factors in the evaluation matrix of SWOT could select the strategies countering weaknesses and threats. The results of the measurement of the attractiveness of defense strategies with selected SWOT factors in the QSPM matrix from experts' viewpoints showed that the impact of external factors on forest management was more than internal factors; hence prioritized strategies were reselected regarding the main stakeholders and the social view of the forest. Based on the weighted average of internal factors, the strategy of the necessity of increasing forest adaptability to climate change was chosen as the first priority, indicating the necessity of science-based management in the Hyrcanian forests.

Planning for management of the forests in the future is not possible without having sufficient information about the forest degradation conditions in the past. The present study was conducted to model the forest degradation levels using logistic regression in the Masal watershed NO: 12. In this study, the factors of slope, altitude, geographic direction, distance from rivers, distance from the road networks, distance from residential areas, population centers and distance from barns as independent variables and different degrees of forest degradation entered into the regression model as dependent variables. Extraction of independent variables is done by using the digital maps of the area and recording the forest degradation degrees is done through terrestrial field work. To determinate the impact of each factor a hierarchical analysis method and to model the factors the logistic regression with five connects functions including: Cauchit, Negative log-log, Complementary log-log, Logit and Probit were used. The statistical tests of similarity and Wald were used to examine the significance of the model and its coefficients. The results showed that the model constructed with the Probit connection function has the appropriate capability in modeling the forest degradation. The results also showed that 20760.32 hectares of studied forests (equivalent to 83 percent of the total forest area) were degraded from very low to moderate levels. As a result, the conservation plans for 83% of the watershed forest areas and the implementation of recovery operations for 17% of the forest area (according to the forest degradation map presented in this study) have a favorable effect on improvement of the forest condition in the studied watershed.

Nowadays، fires are the most common damaging factor of natural ecosystems after urban and agricultural human activities. The aim of this study is to identify the susceptible areas to fires using Discriminant Analysis and Adaptive Network Based Fuzzy Inference System in the Golestan National Park as one of the most important biosphere reserves. The required data and digital layers were prepared from associated sites، organizations and field surveys in the area of the study. After preparing the data by assuming the occurred fire، the most significant effective factors were identified using Discriminate Analysis. These factors were calculated as a weighted average and models were implemented by using K-Fold Algorithm with trapezoidal، Gaussian، triangular and bell membership functions in six stages. The best model was used for simulation. The fire hazard map was prepared with five classifications، very low، low، medium، high and very high. An accuracy assessment was performed using the relative operating characteristic. The results of the Discriminant Analysis showed the important factors، including presence of hunters and shepherds، distance from roads، average temperature، distance from the springs، rainfall during the growing season and slope. Validation trapezoidal membership functions showed best results with value of R2= 0. 534 and RMSE= 0. 283. The results of the accuracy assessment obtained with value of ROC= 0. 875. While the very High-risk area was 972 hectares، and High-risk area was 16879 hectares of 91895 hectares area. According to accuracy of the proposed map، it can be used to control the region fires in Golestan national park.